Efficient Joint Estimation of Tracer Distribution and Background Signals in Magnetic Particle Imaging using a Dictionary Approach

TL;DR

This paper introduces a novel dictionary-based joint estimation method for background signals in magnetic particle imaging, enabling accurate background correction even with non-linear drift, thereby reducing artifacts and improving sensitivity.

Contribution

It presents a new approach that jointly estimates particle distribution and background signals using a dictionary derived from calibration scans, handling non-linear background drift effectively.

Findings

Strong suppression of background artifacts in phantom experiments

Effective removal of direct feed-through of excitation field

Allows background estimation without proximity constraints

Abstract

Background signals are a primary source of artifacts in magnetic particle imaging and limit the sensitivity of the method since background signals are often not precisely known and vary over time. The state-of-the art method for handling background signals uses one or several background calibration measurements with an empty scanner bore and subtracts a linear combination of these background measurements from the actual particle measurement. This approach yields satisfying results in case that the background measurements are taken in close proximity to the particle measurement and when the background signal drifts linearly. In this work, we propose a joint estimation of particle distribution and background signal based on a dictionary that is capable of representing typical background signals and allows for precise estimation of the background even when the latter is drifting non-linearly over time. Using a singular-value decomposition, the dictionary is derived from a large number of background calibration scans that do not need to be recorded in close proximity to the particle measurement. The dictionary is sufficiently expressive and represented by its principle components. The proposed joint estimation of particle distribution and background signal is expressed as a linear Tikhonov-regularized least squares problem, which can be efficiently solved. In phantom experiments it is shown that the method strongly suppresses background artifacts and even allows to estimate and remove the direct feed-through of the excitation field.